JDS Journal of Data Science 1683-86021680-743X1680-743X School of Statistics, Renmin University of China JDS1066 10.6339/22-JDS1066 Data Science in Action The Impact of COVID-19 on Subjective Well-Being: Evidence from Twitter Data CarpiTiziana1 HinoAiro2 IacusStefano Mariasiacus@iq.harvard.edu3∗ PorroGiuseppe4 Department of Studies in Language Mediation and Intercultural Communication, University of Milan, Piazza Indro Montanelli 1, 20099 Milan, Italy School of Political Science and Economics, Waseda University, 1-6-1 Nishiwaseda Shinjuku-ku, Tokyo 169-8050, Japan Institute for Quantitative Social Science, Harvard University, 1737 Cambridge St, Cambridge, MA 02138, USA Department of Law, Economics and Culture, University of Insubria, Via Sant’Abbondio 12, 22100 Como, Como, Italy Corresponding author. Email: siacus@iq.harvard.edu. 20232992022214761780 Supplementary Material

The supplementary material consists of the following sections: Construction of the Twitter indicators; Stochastic analysis; Dynamic Elastic Net and Dynamic variable selection for the SWB-I/J indicators; Structural equation models. As well as information on authors’ contribution and data availability.

14120221692022 2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.2023 Open access article under the CC BY license.

This study analyzes the impact of the COVID-19 pandemic on subjective well-being as measured through Twitter for the countries of Japan and Italy. In the first nine months of 2020, the Twitter indicators dropped by 11.7% for Italy and 8.3% for Japan compared to the last two months of 2019, and even more compared to their historical means. To understand what affected the Twitter mood so strongly, the study considers a pool of potential factors including: climate and air quality data, number of COVID-19 cases and deaths, Facebook COVID-19 and flu-like symptoms global survey data, coronavirus-related Google search data, policy intervention measures, human mobility data, macro economic variables, as well as health and stress proxy variables. This study proposes a framework to analyse and assess the relative impact of these external factors on the dynamic of Twitter mood and further implements a structural model to describe the underlying concept of subjective well-being. It turns out that prolonged mobility restrictions, flu and Covid-like symptoms, economic uncertainty and low levels of quality in social interactions have a negative impact on well-being.

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